Data‐Driven Modeling of Atomic Oxygen Airglow over a Period of Three Solar Cycles

Mackovjak, Š.; Varga, Matej; Hrivňak, Stanislav; Palkoci, Ondrej; Didebulidze, G. G.

doi:10.1029/2020ja028991

Cited by 5 publications

(3 citation statements)

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“…Thirty years of airglow data at OI 557.7 nm and OI 630.0 nm have been used to establish a data driven model consistent with the GLOW airglow model. The result is the ability to visualize seasonal and solar cycle variations in red and green oxygen lines 21 , 22 . From 1998 to 2001, airglow emissions OI5577, O2b(0,1), and OH(6,2) and the rotational temperature of the OH band, at an equatorial location, show semiannual variations with maxima at the equinoxes and minima at the solstices 23 .…”

Section: Introductionmentioning

confidence: 99%

Linking solar minimum, space weather, and night sky brightness

Grauer

A.²

2021

Sci Rep

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This paper presents time-series observations and analysis of broadband night sky airglow intensity 4 September 2018 through 30 April 2020. Data were obtained at 5 sites spanning more than 8500 km during the historically deep minimum of Solar Cycle 24 into the beginning of Solar Cycle 25. New time-series observations indicate previously unrecognized significant sources of broadband night sky brightness variations, not involving corresponding changes in the Sun's 10.7 cm solar flux, occur during deep solar minimum. New data show; (1) Even during a deep solar minimum the natural night sky is rarely, if ever, constant in brightness. Changes with time-scales of minutes, hours, days, and months are observed. (2) Semi-annual night sky brightness variations are coincident with changes in the orientation of Earth's magnetic field relative to the interplanetary magnetic field. (3) Solar wind plasma streams from solar coronal holes arriving at Earth’s bow shock nose are coincident with major night sky brightness increase events. (4) Sites more than 8500 km along the Earth's surface experience nights in common with either very bright or very faint night sky airglow emissions. The reason for this observational fact remains an open question. (5) It is plausible, terrestrial night airglow and geomagnetic indices have similar responses to the solar energy input into Earth's magnetosphere. Our empirical results contribute to a quantitative basis for understanding and predicting broadband night sky brightness variations. They are applicable in astronomical, planetary science, space weather, light pollution, biological, and recreational studies.

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Section: Introductionmentioning

confidence: 99%

Linking solar minimum, space weather, and night sky brightness

Grauer

A.²

2021

Sci Rep

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“…This will also lead to a better understanding of the effects of the terrestrial magnetic field, and meteorological or seismogenic events on the propagation of sferics and on the properties of the D-layer. Therefore it might significantly contribute also to the study of thermosphere-ionosphere variations presented by Mackovjak et al (2021). Even more, such extensive analysis of several years of VLF data has not been performed yet, and could bring interesting information about diurnal and seasonal variability of signal propagation effects, as well as their dependence on different phases of the solar cycle.…”

Section: Discussionmentioning

confidence: 99%

Automatic Detection of Atmospherics and Tweek Atmospherics in Radio Spectrograms Based on a Deep Learning Approach

Maslej-Krešňáková

Kundrát

Mackovjak

et al. 2021

Earth and Space Science

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Lightning strikes can be routinely observed by the radio receivers that measure electromagnetic signals in the very low frequency range. The acquired pulses called atmospherics provide valuable details about source lightning discharges and also about the Earth‐ionosphere waveguide where they can propagate thousands of kilometers. The automatic acquisition of the events requires also automatic methods for extraction of atmospherics' details to confirm the observed trends with statistical significance. For this purpose, we have developed a method based on a deep learning approach to automatically obtain the type of atmospherics, their exact time, and the frequency range from the frequency‐time spectrograms. The method that employs convolutional neural networks is designed to be adaptable to scientific needs and provide reliable results according to the requirements on the sensitivity to events, computation performance, and precision of extracted details. The efficiency and specific steps of our method are demonstrated for data recorded by the ELMAVAN‐G instrument. The comprehensive description of the method allows its usage for regular characterization of the ionospheric D‐layer or for other similar applications in the future.

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“…Firstly, modeling of airglow intensity variation in the Earth's upper atmosphere as it is directly influenced by the amount of solar radiation in ultraviolet spectral range. The segmented solar corona structures will be additional input features for the model developed by Mackovjak et al (2021). The automatically obtained parameters like total area size, heliographic coordinates, and lifetime of specific coronal structures might provide very valuable supplemental information to the actually employed F10.7 index that represents general solar activity.…”

Section: Discussionmentioning

confidence: 99%

SCSS-Net: Solar Corona Structures Segmentation by Deep Learning

Mackovjak,

Harman,

Maslej-Krešňáková

et al. 2021

Preprint

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Structures in the solar corona are the main drivers of space weather processes that might directly or indirectly affect the Earth. Thanks to the most recent space-based solar observatories, with capabilities to acquire high-resolution images continuously, the structures in the solar corona can be monitored over the years with a time resolution of minutes. For this purpose, we have developed a method for automatic segmentation of solar corona structures observed in EUV spectrum that is based on a deep learning approach utilizing Convolutional Neural Networks. The available input datasets have been examined together with our own dataset based on the manual annotation of the target structures. Indeed, the input dataset is the main limitation of the developed model's performance. Our SCSS-Net model provides results for coronal holes and active regions that could be compared with other generally used methods for automatic segmentation. Even more, it provides a universal procedure to identify structures in the solar corona with the help of the transfer learning technique. The outputs of the model can be then used for further statistical studies of connections between solar activity and the influence of space weather on Earth.

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Data‐Driven Modeling of Atomic Oxygen Airglow over a Period of Three Solar Cycles

Cited by 5 publications

References 63 publications

Linking solar minimum, space weather, and night sky brightness

Linking solar minimum, space weather, and night sky brightness

Automatic Detection of Atmospherics and Tweek Atmospherics in Radio Spectrograms Based on a Deep Learning Approach

SCSS-Net: Solar Corona Structures Segmentation by Deep Learning

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